An innovative coverage optimization method for smart information monitoring in agricultural IoT using the multi-strategy Pelican optimization algorithm

With the rapid advancement of agricultural technology, Agricultural Wireless Sensor Network (AWSN) monitoring for crop growth in large-scale fields has become pivotal in smart agriculture. Optimizing AWSN coverage is crucial for enhancing production efficiency and resource utilization. However, traditional optimization algorithms struggle with local convergence and accuracy in large-scale sensor deployment, an NP-hard problem. To address this, we propose a novel Multi-Strategy Pelican Optimization Algorithm (MSPOA), integrating a good point set strategy, a 3D spiral Lévy flight strategy, and an adaptive T-distribution variation strategy. The good point set strategy expands the search range and enhances local search capability, while the 3D spiral Lévy flight strategy improves convergence speed and global search accuracy. The adaptive T-distribution variation strategy further boosts global search ability, and pelican-inspired movement and collaboration strategies enhance adaptability and robustness in diverse agricultural scenarios. Comparative experiments with Improved Artificial Bee Colony Algorithm (IABC), Chaotic Adaptive Firefly Optimization Algorithm (CAFA), Adaptive Particle Swarm Optimization (APSO), and Lévy Flight Strategy Chaotic Snake Optimization Algorithm (LCSO) demonstrate that MSPOA improves network coverage by 5.85%, 11.33%, 21.05%, and 20.66%, respectively. Additionally, MSPOA exhibits strong adaptability and stability in dynamic agricultural environments.